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Running the application

With the addition of feature extraction in our pipeline, we first need to perform feature extraction on the files:

  1. Assuming the folder of files called temp_data exists, execute the following command:
PS chapter03-logistic-regression\bin\Debug\netcoreapp3.0> .\chapter03-logistic-regression.exe extract temp_data                                                
Extracted 8 to sampledata.csv

The output shows the count of extracted files and the output sample file.

  1. To train the model using either the included sampledata.csv or one you trained yourself, execute the following command:
PS chapter03-logistic-regression\bin\Debug\netcoreapp3.0> .\chapter03-logistic-regression.exe train ..\..\..\Data\sampledata.csv

The chapter3.mdl model file should exist in the folder executed in once complete.

  1. To run the newly trained model against an existing file such as the compiled chapter3 executable, run the following command:
PS chapter03-logistic-regression\bin\Debug\netcoreapp3.0> .\chapter03-logistic-regression.exe predict .\chapter03-logistic-regression.exe                      
Based on the file (.\chapter03-logistic-regression.exe) the file is classified as benign at a confidence level of 8%
If you are looking for sample files, the c:\Windows and c:\Windows\System32 folders contain numerous Windows Executables and DLLs. In addition, if you are looking to create malicious-looking files that are actually clean, you can create files on the fly on http://cwg.io in various file formats. This is a helpful tool in the cyber-security space where testing new functionality on a development machine is much safer than detonating real zero-day threats on!
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